Reasoning System GraphScale

Within a semantic solution pure data will be enriched with an ontology to become a knowledge net. The latter is the input to a logical reasoning process able to derive knowledge out of data. GraphScale is such a high-performance reasoning and querying system for large knowledge nets stored in triple stores or relational resp. NoSQL databases. With GraphScale technology existing storage systems are upgraded to efficient and reliable processing components for knowledge intensive applications.

More Scalability and Expressivity for Smart Data

GraphScale is explicitly designed to process large knowledge nets following the expressive OWL 2 RL language fragment. The GraphScale technology is based on an novel abstraction approach, that has performance advantages in comparison to mechanisms in known triple stores and is provable sound and complete. The result is a flexible semantic system build on top of existing data stores for efficiently deriving all logical consequences (materialization). As a consequence, SPARQL queries can be executed directly on the data store or via the GraphScale abstraction index. To meet high-performance requirements GraphScale implements a parallel processing architecture from the ground up and can benefit from distributed data management of the underlying data stores such as replication or sharding. For maximal performance GraphScale can be set up as a pure in-memory system.